In the last 20 years, there has been an explosion of knowledge about the science of aging and age-related disease. All life forms on earth have evolved through natural selection, which selects the best genotype for fitness in a particular ecological niche. In 1952 the British Nobel zoologist Peter Medawar proposed that aging is the simple result of the failure of natural selection to maintain fitness in older animals with declining fertility. As fertility wanes, then the chances to correct inappropriate gene expression via natural selection also decline, generating the aging phenotype. Thus, according to Medawar's hypothesis, aging is indirectly caused by the declining forces of natural selection to select the best fitness genes for the aged animal as reproduction capacity declines. In 1957, George Williams further developed Medawar's evolutionary theory of aging by introducing the concept of antagonistic pleiotropy, wherein a gene may promote fitness in young fertile animals (and thus be selected for) but become a liability late in life leading to a subsequent decline in fitness. Modern versions of Medawar's and William's evolutionary theories of aging are still widely believed today by most experts in aging science, as the theory fits well with the immense body of literature showing that natural selection is responsible for virtually all of the phenotypes present in the diverse species observed in Nature. Evolution appears to evolve a life history for each species that is best adapted to its ecological niche.
Besides its sound theoretical basis in the well-known mechanisms of natural selection, the Evolution Theory of Aging has also been directly tested in Drosophila melanogaster by Michael Rose at UCI. If the Evolution Theory of Aging was correct, Dr. Rose predicted that he should be able to select populations of long lived animals by simply selecting for reproductive longevity. To carry out his longevity experiment, Dr. Rose started with 5 lines of wild type Drosophila flies and selected for reproductive longevity over a 27 year period. Within a few years, Dr. Rose could tell that he was successfully generating longer lived flies by selecting each generation for late fertility. He finally obtained robust Methuselah flies with a demonstrated lifespan of some 3 to 4 times that found in the non-selected control lines, while retaining fertility and sexual vitality.
Several independent experiments have confirmed that these Methuselah flies are indeed long lived compared to wild type flies. These studies show that the selected Methuselah 0 and SO flies have about a 300% longer mean lifespan than the non-selected wild type B flies. This selection experiment is a dramatic verification that evolution has dominating effects on the aging process.
Current genetic work on Methuselah flies has shown that several hundred genes have an altered expression when selecting for longevity. These experimental results are fully consistent with the Evolution Theory of Aging, which predicts that aging leads to poorly functioning organisms as natural selection to correct optimal gene function wanes with age. Research suggests that the alterations in the expression of scores of genes during aging require a multipronged treatment strategy to address the large number of changes in gene expression. Since aging is closely linked with age-related diseases, the hundreds of gene expression changes with age put into question the dominant paradigm of the pharmaceutical industry to develop drug treatments for particular age-related disease using a single compound to target a single biochemical pathway.
Because so many of the identified age-related longevity genes are linked to neural function, there exists a need for multi-gene targeted treatments for memory loss, cognition, neural function, and neural disease. The present combination of herbal extracts and highly purified components of plants target as many of the complementary age-related, neural genetic pathways as possible and act on genetic pathways that were discovered by analyzing genetic changes in long lived fly populations and using machine learning to analyze human gene banks on aging populations. Potential treatments for various neural dysfunctions were identified using existing screening assays for beta-amyloid, tau, and Park Drosophila models of Alzheimer's disease or Parkinson's disease.